PCA is simply Eigen vector extraction on covariance matrix. While more impressiv... | Hacker News

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		toxik on Oct 5, 2019 \| parent \| context \| favorite \| on: Principal Component Analysis PCA is simply Eigen vector extraction on covariance matrix. While more impressive techniques exist, PCA is so simple it will never be passé.

rectangletangle on Oct 5, 2019 | [–]

Agreed, there's something to be said for simple models that are "good enough," especially when their limitations are clear. k-NN also comes to mind.

beagle3 on Oct 5, 2019 | [–]

Indeed. And e.g. generalized eigen problem extend this to the case of two competing data sets.

Ignoring the eigen aspect would miss a lot of both theory and practice.

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